Analysis of blasted rocks fragmentation using digital image processing (Case study: Limestone quarry of Obajana Cement Company)

Kayode A. Idowu¹, Boluwaji M. Olaleye², Muyideen A. Saliu²

¹University of Jos, Jos, 930003, Nigeria

²Federal University of Technology, Akure, 340252, Nigeria

Min. miner. depos. 2021, 15(4):34-42

https://doi.org/10.33271/mining15.04.034

Full text (PDF)

      ABSTRACT

      Purpose. Blasting is an important aspect of mining activities in which fragmentation is the key component that determines its efficiency. Fragmentation is the first result of blasting, and is directly related to the costs of mining.

      Methods. There are two basic methods for determining the degree of rock fragmentation, the direct and indirect methods. The direct method includes sieve analysis while the indirect method involves observational, empirical and digital image processing methods. The digital image processing method with the aid of Split Desktop software was used in this study, to analyze the size of fragmentation in Obajana limestone quarry. Two pits of similar line of operation were considered.

      Findings. In each of the pits considered, five muckpiles of blasted rocks after blasting with different blasting patterns were analyzed to study the fragmentation phenomenon. The F₈₀ and F₉₀ values from the Split Desktop image analysis for the 5×3 m and 4×3 m in Pit 1 and Pit 2 were approximately 87.96 and 96.20 cm; and 91.34 and 98.66 cm respectively. Also, the F₈₀ and F₉₀ values obtained from the Kuz-Ram model for the 5×3 m and 4×3 m of Pit 1and Pit 2 were 99.9967 and 99.9994 cm; and 99.9957 and 99.9993 cm respectively. The results of the Split Desktop were compared to the results of the Kuz-Ram experiential model. The values of F₈₀ and F₉₀ of the blasted rocks are very close to the crusher gape value of 1 m, which reduces the production costs, and that is an outcome practically realized for the two pits of Obajana quarry.

      Originality. The findings showed that the output obtained from the Split Desktop software which is a digital image processing method were in conformity with the Kuz-Ram experiential model which is based on empirical relationship.

      Practical implications. In conclusion, the results of the investigation have significant implications for the practical application. It gives more options to explore for rock blast fragmentation efficiency of the desired area.

      Keywords: blasting, fragmentation, muckpile, limestone deposit, digital image processing, desktop software

      REFERENCES

1. Singh, S.P., Narendrula, R., & Duffy, D. (2005). Influence of blasted muck on the performance of loading equipment. In 3^rd EFEE World Conference on Explosives and Blasting (pp. 347-355). Brighton, United Kingdom: European Federation of Explosives Engineers.
2. Voulgarakis, A.G., Michalakopoulos, T.N., & Panagiotou, G.N. (2016). The minimum response time in rock blasting: a dimensional analysis of full-scale experimental data. Mining Technology, 125(4), 242-248. https://doi.org/10.1080/14749009.2016.1175163
3. Shehu, S.A., Yusuf, K.O., & Hashim, M.H.M. (2020). Comparative study of WipFrag image analysis and Kuz-Ram empirical model in granite aggregate quarry and their application for blast fragmentation rating. Geomechanics and Geoengineering, 1-10. https://doi.org/10.1080/17486025.2020.1720830
4. Lawal, A.I. (2020). An artificial neural network-based mathematical model for the prediction of blast-induced ground vibration in granite quarries in Ibadan, Oyo State, Nigeria. Scientific African, (8), e00413. https://doi.org/10.1016/j.sciaf.2020.e00413
5. Lawal, A.I., Kwon, S., & Kim, G.Y. (2021). Prediction of the blast-induced ground vibration in tunnel blasting using ANN, moth-flame optimized ANN, and gene expression programming. Acta Geophysica, 69(1), 161-174. https://doi.org/10.1007/s11600-020-00532-y
6. Siddiqui, F., Shah, S., & Behan, M. (2009). Measurement of size distribution of blasted rock using digital image processing. Journal of King Abdulaziz University. Engineering Sciences, 20(02), 81-93. https://doi.org/10.4197/Eng.20-2.4
7. Lawal, A.I., & Kwon, S. (2020). Application of artificial intelligence in rock mechanics: An overview. Journal of Rock Mechanics and Geotechnical Engineering, 13(1), 248-266. https://doi.org/10.1016/j.jrmge.2020.05.010
8. Lawal, A.I. (2021). A new modification to the Kuz-Ram model using the fragment size predicted by image analysis. International Journal of Rock Mechanics and Mining Sciences, (138), 104595. https://doi.org/10.1016/j.ijrmms.2020.104595
9. Strelec, S., Gazdek, M., & Mesec, J. (2011). Blasting design for obtaining desired fragmentation. Technical Gazette, 18(1), 79-86.
10. Kuznetsov, V.M. (1973). The mean diameter of the fragments formed by blasting rock. Soviet Mining Science, 9(2), 144-148. https://doi.org/10.1007/BF02506177
11. Badrud, Din M. (2011). Analysis of gradation methods for blast fragmentation rocks. International Scientific Professional Quarterly of Mining Engineering, (30), 32-35.
12. Hashemi, S., Kariminasab, S., Shayestehfar, M., & Seyyed Baqeri, S.R. (2009). Assessment of ability of gold size to determine gradation of fine aggregates. In 4^th University Conference on Mining Engineering.
13. Bamford, T., Esmaeili, K., & Schoellig, A.P. (2017). A real-time analysis of post-blast rock fragmentation using UAV technology. International Journal of Mining, Reclamation and Environment, 31(6), 439-456. https://doi.org/10.1080/17480930.2017.1339170
14. Maerz, N.H. (1996). Reconstructing 3-D block size distributions from 2-D measurements on sections. In FRAGBLAST 5 Workshop on Measurement of Blast Fragmentation (pp. 39-43). Montreal, Quebec, Canada: Taylor and Francis. https://doi.org/10.1201/9780203747919-7
15. Maerz, N.H., & Zhou, W. (1998). Optical digital fragmentation measuring systems- inherent sources of error. International Journal for Blasting and Fragmentation (Fragblast), 2(4), 415-431. https://doi.org/10.1080/13855149809408786
16. Tavakol, E.A., & Hosseini, M. (2017). Analysis of blasted rocks fragmentation using digital image processing (Case study: Limestone quarry of Abyek Cement Company). International Journal of Geo-Engineering, 8(16), 1-10. https://doi.org/10.1186/s40703-017-0053-z
17. Amirshenava, M., & Moomivand, H. (2014). Determining fragmentation of minerals and blasting index (BI) using image analysis in the latest split desktop for zone no. 5 of Rashkan mine. In International 5^th Conference on Rock Mechanics. Tehran, Iran: Tarbiat Modarres University.
18. Bobo, T. (2010). What’s new with the digital image analysis software split-desktop. Split engineering, LLC, Tucson.
19. Jatau, B.S., & Oyinloye, O.T. (2003). Reserve estimation of Oyo/Iwah marble deposit near Obajana, Kogi State, Nigeria. Nigerian Mining and Geosciences Society 39^th Annual International Conference, 1-5.
20. Iloeje, N.P. (1991). New geography of Nigeria. Longman, Lagos-Ibadan, 201 pp.
21. Souza, J.C., Silva, A.C.S., & Rocha, S.S. (2018). Analysis of blasted rocks prediction and rock fragmentation results using Split-desktop software. Association of Brasilian Metallurgical, Materials and Minerals. CC BY-NC-ND, (4), 1523-2176.
22. Jimeno, C.L., Jimeno, E.L., & Carcedo, F.J.A. (1995). Drilling and blasting of rocks. Rotterdam, Netherlands: CRC Press.
23. Gheibie, S., Aghababaei, H., Hoseinie, S.H., & Pourrahimian, Y. (2009). Modified Kuz-Ram fragmentation model and its use at the Sungun Copper Mine. International Journal of Rock Mechanics and Mining Sciences, 46(6), 967-973. https://doi.org/10.1016/j.ijrmms.2009.05.003
24. Akbari, M., Lashkaripour, G., Yarahamdi Bafghi, A., & Ghafoori, M. (2015). Blastability evaluation for rock mass fragmentation in Iran central iron ore mines. International Journal of Mining Science and Technology, 25(1), 59-66.https://doi.org/10.1016/j.ijmst.2014.11.008